Presentación

A partir del análisis realizado por la Dra.

Primer Análisis

Sección 1 - Página Principal

Data Analysis module allows the user to have a preliminary analysis for quality control based on the data distribution per sample, using plots below

CPM, Counts Per Million Plot Fig. 1. CPM, Counts Per Million Plot

The Count per million (CPM) plot shows the number of genes within each sample having more than 0 CPM, or more than 1, 2, 5 and 10 CPM. This plot could help the user decide the threshold to remove genes that appear to be very lowly expressed in any of the experimental conditions.

Boxplot with pseudo counts Fig. 2. Boxplot with pseudo counts

Samples boxplot provides an easy way to visualize the distribution of pseudocounts in each sample, since it shows statistical measures such as median, quartiles, minimum and maximum. Whiskers are also drawn extending beyond each end of the box with points beyond the whiskers typically indicating outliers.

Density plot Fig. 3. Density plot

Pseudocounts distributions can also be summarized by means of a density plot. Density plot provides more detail by enabling, for example, the detection of dissimilarity in replicates’ behavior.

Multi-dimensional scaling plot Fig. 4. Multi-dimensional scaling plot

Multidimensional Scaling (MDS) is a technique that is used to create a visual representation of the pattern of proximities (similarities, dissimilarities, or distances) among a set of objects. In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, distance between sample labels indicates dissimilarity.

Principal component plot Fig. 5. Principal component plot

This type of plot is useful for visualizing the overall effect of experimental covariates and batch effects. In the context of RNA-Seq analysis, PCA essentially clusters samples by groups of the most significantly deregulated genes. Clustering first by the most significant group, then by progressively less significant groups.

Boxplot of standardized counts with TMM Fig. 6. Boxplot of standardized counts with TMM

Multi-dimensional scaling plot of standardized counts with TMM Fig. 7. Multi-dimensional scaling plot of standardized counts with TMM

Principal component plot of standardized counts with TMM Fig. 8. Principal component plot of standardized counts with TMM

This type of graph is useful for visualizing the overall effect of experimental covariances and their batch effects. When a pair of samples, under the same condition, tend to group together, a lot effect can be suspected.

From this point, the same graphs are replicated, but they show the raw counts normalized with the TMM method (Trimmed Mean of M-values), (1) Robinson.

Sección 2 - EdgeR.php

In this section, we present the results of the differential expression analysis generated by the edgeR method. (1)

Result Files

The following text files contain the results of differential expression analysis for various comparisons:

The text files can be opened in Excel and contain the result of the differential expression analysis for all genes with significant counts. The meaning of each column is as follows:

  1. id: The gene ID (gene, transcript, etc.)
  2. log2FoldChange: The log fold-change between conditions being tested
  3. logCPM: Average log2-counts per million, averaged over all libraries
  4. p value: The statistical significance of the change
  5. FDR: False Discovery Rate adjusted for multiple testing with the Benjamini-Hochberg procedure

The last columns correspond to:

  1. raw counts: Raw counts for all samples
  2. normalized counts: Normalized counts for all samples
  3. Regulation: Gene regulation, indicating how and in which condition the gene was expressed

Subgroup Analysis

Additional analysis files:

These “TOP.txt” files represent subgroup analyses and contain results only for differentially expressed genes based on the established cutoff parameters.

The following files have genes that it was reported as differential expressed for more than one method,(EdgeR + other)

Gene Expression Plots

In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, with the distance between sample labels indicating dissimilarity. When the experiment is well controlled, the greatest sources of variation should correspond to the treatments/groups of interest.

The Smear plot visualizes the results of a DE analysis, showing the log-fold change against log-counts per million. Differentially expressed features are highlighted in red.

The Volcano plot summarizes both fold-change and p-values. It is a scatter-plot of the negative log10-transformed p-values against the log2 fold change. Features declared as differentially expressed are highlighted in red.

Imagen 0{:target=“_blank”} Imagen 1{:target=“_blank”} Imagen 2{:target=“_blank”} Imagen 3{:target=“_blank”} Imagen 4{:target=“_blank”} Imagen 5{:target=“_blank”} Imagen 6{:target=“_blank”} Imagen 7{:target=“_blank”} Imagen 8{:target=“_blank”} Imagen 9{:target=“_blank”} Imagen 10{:target=“_blank”} Imagen 11{:target=“_blank”} Imagen 12{:target=“_blank”} Imagen 13{:target=“_blank”} Imagen 14{:target=“_blank”}

Sección 3 - DESeq2.php

DESeq2 Results

Diferential Expression by DESeq2. (1)

The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of e ach column is described below.

  1. id : The first column is the id(gene, transcript, etc.)
  2. baseMean: Mean normalised counts, averaged over all samples from the two conditions
  3. log2FoldChange : logaritmo de la magnitud de cambio entre el par de condiciones, en la tercer columna
  4. lfcSE: Standard errors of logarithm fold change
  5. Stat: test statistics
  6. pvalue: p value for the statistical significance of this change
  7. padj: p value adjusted for multiple testing with the Benjamini-Hochberg procedure The last columns correspond to:
  8. raw counts: Raw counts for all sample
  9. normalized counts: Normalized counts for all samples
  10. Regulation : gene regulation, indicating how and in which condition the genes was expressed

The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters).

The following files have genes that it was reported as differential expressed for more than one method (DESeq2 + other)

Graficas

plotPCA.pdfThis type of plot is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).

plotMA.pdf, This plot represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014). References:

Imagen 1{:target=“_blank”} Imagen 2{:target=“_blank”} Imagen 3{:target=“_blank”} Imagen 4{:target=“_blank”} Imagen 5{:target=“_blank”} Imagen 6{:target=“_blank”} Imagen 7{:target=“_blank”} Imagen 8{:target=“_blank”} Imagen 9{:target=“_blank”} Imagen 10{:target=“_blank”}

Sección 4 - limma.php

limma Results

In this section the differential expression results generated by the limma method are shown.(1)

The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :

id : The first column is the id(gene, transcript, etc.), log2FoldChange : The log fold-change between conditions being tested AveExpr: Average log2 expression t: the t-statistic used to assess differential expression P.Value: The p-value for differential expression; this value is not adjusted for multiple testing adj.P.Val :The p-value adjusted for multiple testing B: The B-statistic is the log-odds that the gene is differentially expressed The last columns correspond to: raw counts: Raw counts for all sample normalized counts: Normalized counts for all samples Regulation : gene regulation, indicating how and in which condition the genes was expressed

The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters). The following files have genes that it was reported as differential expressed for more than one method,(limma + other)

Graph PlotPCA

the graph plotPCA.pdf is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).

In plot plotMA.pdf it represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014).

Click to enlarge the image

Imagen 1{:target=“_blank”} Imagen 2{:target=“_blank”} Imagen 3{:target=“_blank”} Imagen 4{:target=“_blank”} Imagen 5{:target=“_blank”} Imagen 6{:target=“_blank”} Imagen 7{:target=“_blank”} Imagen 8{:target=“_blank”} Imagen 9{:target=“_blank”} Imagen 10{:target=“_blank”}

Sección 5 - NOISeq.php

NOISeq Results

In this section the differential expression results generated by the NOISeq method are shown. (1)

The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :

  1. id : The first column is the id(gene, transcript, etc.)
  2. Cond1_mean: The second column is the mean of the biological replicates of first condition
  3. Cond2_mean: the thirds column is the mean of the biological replicates of secondth condition
  4. theta: the fourth column is a Differential expression statistics
  5. Prop: the fifth column show Probability of differential expression
  6. log2FoldChange : The log fold-change between conditions being tested The last columns correspond to:
  7. raw counts: Raw counts for all sample
  8. normalized counts: Normalized counts for all samples
  9. DE: gene regulation, indicating how and in which condition the genes was expressed

Subgrupos

The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters).

Differential expressed

The following files have genes that it was reported as differential expressed for more than one method,(NOISeq + others)

Sección 6 - VennDiagram.php

Results Integration

Once the results of the differential expression analyses has been obtained (by the different selected methods), these results are compared, by mean of Venn, bar and correlograms plots, in order to see how the different methods agree on the final list of differential expressed genes

In the process of integration of results, different files are obtained. These files contain information related to the differentially expressed genes found at the intersection of all methods. The description of the files is as follows:

Files with _table.txt ending: File of binary values, which indicate for each gene, the methods that report it as differentially expressed. In the last column of the file, a description of the gene regulation can be found, where is indicated how and in which condition the genes was expressed.

Files with _IntersectSummary.txt ending: Contains the summary of the number of DE genes in all possible logical relationships between the different methods

Files with _Intesrsect_TOP_IDs.txt ending: Table that contains the ID of those genes that are at the intersection of the DE genes obtained by all the different selected methods.

Files with _logFCTable.txt ending: Table containing the logFC values of the DE genes reported for all methods

Files with _AbundanceTable.txt ending: Table containing the raw and normalized counts of all samples of the DE genes reported for all methods

Files with _PvalTable.txt ending: Table containing the padjust/FDR values of the DE genes reported for all methods

The Venn diagram shows the intersection of the differentially expressed genes reported by each of the selected methods. Further analysis could be considered only in the list of genes that were identified as differentials expressed by more than one method. (In the center of the graph)

Diagramas de Venn

  1. Imagen 1{:target=“_blank”}
  2. Imagen 2{:target=“_blank”}
  3. Imagen 3{:target=“_blank”}

Graficos

Files

  1. Imagen 1{:target=“_blank”}
  2. Imagen 2{:target=“_blank”}
  3. Imagen 3{:target=“_blank”}

upSetR files

  1. Imagen 4{:target=“_blank”}
  2. Imagen 5{:target=“_blank”}
  3. Imagen 6{:target=“_blank”}

UnionHeathMap files

  1. Imagen 7{:target=“_blank”}
  2. Imagen 8{:target=“_blank”}
  3. Imagen 9{:target=“_blank”}

pvalCorrelation files

  1. Imagen 10{:target=“_blank”}
  2. Imagen 11{:target=“_blank”}
  3. Imagen 12{:target=“_blank”}

logFCCorrelation files

  1. Imagen 13{:target=“_blank”}
  2. Imagen 14{:target=“_blank”}
  3. Imagen 15{:target=“_blank”}

AbundanceCorrelation files

  1. Imagen 16{:target=“_blank”}
  2. Imagen 17{:target=“_blank”}
  3. Imagen 18{:target=“_blank”}

IntersectHeatmap Files

  1. Imagen 19{:target=“_blank”}
  2. Imagen 20{:target=“_blank”}

Segundo Análisis

Sección 2.1 - Página Principal

Data Analysis module allows the user to have a preliminary analysis for quality control based on the data distribution per sample, using plots below

CPM, Counts Per Million Plot Fig. 1. CPM, Counts Per Million Plot

The Count per million (CPM) plot shows the number of genes within each sample having more than 0 CPM, or more than 1, 2, 5 and 10 CPM. This plot could help the user decide the threshold to remove genes that appear to be very lowly expressed in any of the experimental conditions.

Boxplot with pseudo counts Fig. 2. Boxplot with pseudo counts

Samples boxplot provides an easy way to visualize the distribution of pseudocounts in each sample, since it shows statistical measures such as median, quartiles, minimum and maximum. Whiskers are also drawn extending beyond each end of the box with points beyond the whiskers typically indicating outliers.

Density plot Fig. 3. Density plot

Pseudocounts distributions can also be summarized by means of a density plot. Density plot provides more detail by enabling, for example, the detection of dissimilarity in replicates’ behavior.

Multi-dimensional scaling plot Fig. 4. Multi-dimensional scaling plot

Multidimensional Scaling (MDS) is a technique that is used to create a visual representation of the pattern of proximities (similarities, dissimilarities, or distances) among a set of objects. In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, distance between sample labels indicates dissimilarity.

Principal component plot Fig. 5. Principal component plot

This type of plot is useful for visualizing the overall effect of experimental covariates and batch effects. In the context of RNA-Seq analysis, PCA essentially clusters samples by groups of the most significantly deregulated genes. Clustering first by the most significant group, then by progressively less significant groups.

Boxplot of standardized counts with TMM Fig. 6. Boxplot of standardized counts with TMM

Multi-dimensional scaling plot of standardized counts with TMM Fig. 7. Multi-dimensional scaling plot of standardized counts with TMM

Principal component plot of standardized counts with TMM Fig. 8. Principal component plot of standardized counts with TMM

This type of graph is useful for visualizing the overall effect of experimental covariances and their batch effects. When a pair of samples, under the same condition, tend to group together, a lot effect can be suspected.

From this point, the same graphs are replicated, but they show the raw counts normalized with the TMM method (Trimmed Mean of M-values), (1) Robinson.

Sección 2.2 - EdgeR.php

In this section, we present the results of the differential expression analysis generated by the edgeR method. (1)

Gene Expression Plots

In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, with the distance between sample labels indicating dissimilarity. When the experiment is well controlled, the greatest sources of variation should correspond to the treatments/groups of interest.

The Smear plot visualizes the results of a DE analysis, showing the log-fold change against log-counts per million. Differentially expressed features are highlighted in red.

The Volcano plot summarizes both fold-change and p-values. It is a scatter-plot of the negative log10-transformed p-values against the log2 fold change. Features declared as differentially expressed are highlighted in red.

Imagen 0{:target=“_blank”}

Imagen 1{:target=“_blank”}

Imagen 2{:target=“_blank”}

Imagen 3{:target=“_blank”}

Imagen 4{:target=“_blank”}

Sección 3 - DESeq2.php

DESeq2 Results

Diferential Expression by DESeq2. (1)

The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of e ach column is described below.

  1. id : The first column is the id(gene, transcript, etc.)
  2. baseMean: Mean normalised counts, averaged over all samples from the two conditions
  3. log2FoldChange : logaritmo de la magnitud de cambio entre el par de condiciones, en la tercer columna
  4. lfcSE: Standard errors of logarithm fold change
  5. Stat: test statistics
  6. pvalue: p value for the statistical significance of this change
  7. padj: p value adjusted for multiple testing with the Benjamini-Hochberg procedure The last columns correspond to:
  8. raw counts: Raw counts for all sample
  9. normalized counts: Normalized counts for all samples
  10. Regulation : gene regulation, indicating how and in which condition the genes was expressed

pval files

Abundance files

logFC files

Other files

The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters).

The following files have genes that it was reported as differential expressed for more than one method (DESeq2 + other)

Graficas

plotPCA.pdfThis type of plot is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).

plotMA.pdf, This plot represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014). References:

Imagen 1{:target=“_blank”}

Imagen 2{:target=“_blank”}

Imagen 3{:target=“_blank”}

Imagen 4{:target=“_blank”}

Imagen 5{:target=“_blank”}

Imagen 6{:target=“_blank”}

Imagen 7{:target=“_blank”}

Imagen 8{:target=“_blank”}

Imagen 9{:target=“_blank”}

Imagen 10{:target=“_blank”}

Sección 4 - limma.php

limma Results

In this section the differential expression results generated by the limma method are shown.(1)

The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :

id : The first column is the id(gene, transcript, etc.), log2FoldChange : The log fold-change between conditions being tested AveExpr: Average log2 expression t: the t-statistic used to assess differential expression P.Value: The p-value for differential expression; this value is not adjusted for multiple testing adj.P.Val :The p-value adjusted for multiple testing B: The B-statistic is the log-odds that the gene is differentially expressed The last columns correspond to: raw counts: Raw counts for all sample normalized counts: Normalized counts for all samples Regulation : gene regulation, indicating how and in which condition the genes was expressed

Main Files

pval files

logFC files

Abundance files

The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters). The following files have genes that it was reported as differential expressed for more than one method,(limma + other)

Graph PlotPCA

the graph plotPCA.pdf is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).

In plot plotMA.pdf it represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014).

Click to enlarge the image

Imagen 1{:target=“_blank”}

Imagen 2{:target=“_blank”}

Imagen 3{:target=“_blank”}

Imagen 4{:target=“_blank”}

Imagen 5{:target=“_blank”}

Imagen 6{:target=“_blank”}

Imagen 7{:target=“_blank”}

Imagen 8{:target=“_blank”}

Imagen 9{:target=“_blank”}

Imagen 10{:target=“_blank”}

Sección 5 - NOISeq.php

NOISeq Results

In this section the differential expression results generated by the NOISeq method are shown. (1)

The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :

  1. id : The first column is the id(gene, transcript, etc.)
  2. Cond1_mean: The second column is the mean of the biological replicates of first condition
  3. Cond2_mean: the thirds column is the mean of the biological replicates of secondth condition
  4. theta: the fourth column is a Differential expression statistics
  5. Prop: the fifth column show Probability of differential expression
  6. log2FoldChange : The log fold-change between conditions being tested The last columns correspond to:
  7. raw counts: Raw counts for all sample
  8. normalized counts: Normalized counts for all samples
  9. DE: gene regulation, indicating how and in which condition the genes was expressed

Main Files

pval files

logFC files

Abundance files

The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters). The following files have genes that it was reported as differential expressed for more than one method,(limma + other)

Differential expressed

The following files have genes that it was reported as differential expressed for more than one method,(NOISeq + others)

Graph PlotPCA

the graph plotPCA.pdf is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).

In plot plotMA.pdf it represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014).

Click to enlarge the image

Imagen 1{:target=“_blank”}

Imagen 2{:target=“_blank”}

Imagen 3{:target=“_blank”}

Imagen 4{:target=“_blank”}

Imagen 5{:target=“_blank”}

Imagen 6{:target=“_blank”}

Imagen 7{:target=“_blank”}

Imagen 8{:target=“_blank”}

Imagen 9{:target=“_blank”}

Imagen 10{:target=“_blank”}

Imagen 11{:target=“_blank”}

Imagen 12{:target=“_blank”}

Imagen 13{:target=“_blank”}

Imagen 14{:target=“_blank”}

Imagen 15{:target=“_blank”}

Sección 6 - VennDiagram.php

Results Integration

Once the results of the differential expression analyses has been obtained (by the different selected methods), these results are compared, by mean of Venn, bar and correlograms plots, in order to see how the different methods agree on the final list of differential expressed genes

In the process of integration of results, different files are obtained. These files contain information related to the differentially expressed genes found at the intersection of all methods. The description of the files is as follows:

Files with _table.txt ending: File of binary values, which indicate for each gene, the methods that report it as differentially expressed. In the last column of the file, a description of the gene regulation can be found, where is indicated how and in which condition the genes was expressed.

Files with _IntersectSummary.txt ending: Contains the summary of the number of DE genes in all possible logical relationships between the different methods

Files with _Intesrsect_TOP_IDs.txt ending: Table that contains the ID of those genes that are at the intersection of the DE genes obtained by all the different selected methods.

Files with _logFCTable.txt ending: Table containing the logFC values of the DE genes reported for all methods

Files with _AbundanceTable.txt ending: Table containing the raw and normalized counts of all samples of the DE genes reported for all methods

Files with _PvalTable.txt ending: Table containing the padjust/FDR values of the DE genes reported for all methods

MatrizWeight files

IntersectSummary files

Table files

AbundanceTable files

logFC_Table files

PvalTable files

TOPs files

The Venn diagram shows the intersection of the differentially expressed genes reported by each of the selected methods. Further analysis could be considered only in the list of genes that were identified as differentials expressed by more than one method. (In the center of the graph)

Diagramas de Venn

Graficos

Files

  1. Imagen 1{:target=“_blank”}

  2. Imagen 2{:target=“_blank”}

  3. Imagen 3{:target=“_blank”}

  4. Imagen 4{:target=“_blank”}

  5. Imagen 5{:target=“_blank”}

PvalCorrelation files

  1. Imagen 1{:target=“_blank”}

  2. Imagen 2{:target=“_blank”}

  3. Imagen 3{:target=“_blank”}

  4. Imagen 4{:target=“_blank”}

UpsetR files

  1. Imagen 1{:target=“_blank”}

  2. Imagen 2{:target=“_blank”}

  3. Imagen 3{:target=“_blank”}

  4. Imagen 4{:target=“_blank”}

IntersectionHeathMap Files

  1. Imagen 1{:target=“_blank”}

  2. Imagen 2{:target=“_blank”}

  3. Imagen 3{:target=“_blank”}

UnionHeathMap Files

  1. Imagen 1{:target=“_blank”}

  2. Imagen 2{:target=“_blank”}

  3. Imagen 3{:target=“_blank”}

  4. Imagen 4{:target=“_blank”}

logFCCCorrelation files

  1. Imagen 1{:target=“_blank”}

  2. Imagen 2{:target=“_blank”}

  3. Imagen 3{:target=“_blank”}

  4. Imagen 4{:target=“_blank”}

AbundanceCorrelation files

  1. Imagen 1{:target=“_blank”}

  2. Imagen 2{:target=“_blank”}

  3. Imagen 3{:target=“_blank”}

  4. Imagen 4{:target=“_blank”}

Tercer Análisis

Sección 1 - Página Principal

Data Analysis module allows the user to have a preliminary analysis for quality control based on the data distribution per sample, using plots below

CPM, Counts Per Million Plot Fig. 1. CPM, Counts Per Million Plot

The Count per million (CPM) plot shows the number of genes within each sample having more than 0 CPM, or more than 1, 2, 5 and 10 CPM. This plot could help the user decide the threshold to remove genes that appear to be very lowly expressed in any of the experimental conditions.

Boxplot with pseudo counts Fig. 2. Boxplot with pseudo counts

Samples boxplot provides an easy way to visualize the distribution of pseudocounts in each sample, since it shows statistical measures such as median, quartiles, minimum and maximum. Whiskers are also drawn extending beyond each end of the box with points beyond the whiskers typically indicating outliers.

Density plot Fig. 3. Density plot

Pseudocounts distributions can also be summarized by means of a density plot. Density plot provides more detail by enabling, for example, the detection of dissimilarity in replicates’ behavior.

Multi-dimensional scaling plot Fig. 4. Multi-dimensional scaling plot

Multidimensional Scaling (MDS) is a technique that is used to create a visual representation of the pattern of proximities (similarities, dissimilarities, or distances) among a set of objects. In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, distance between sample labels indicates dissimilarity.

Principal component plot Fig. 5. Principal component plot

This type of plot is useful for visualizing the overall effect of experimental covariates and batch effects. In the context of RNA-Seq analysis, PCA essentially clusters samples by groups of the most significantly deregulated genes. Clustering first by the most significant group, then by progressively less significant groups.

Boxplot of standardized counts with TMM Fig. 6. Boxplot of standardized counts with TMM

Multi-dimensional scaling plot of standardized counts with TMM Fig. 7. Multi-dimensional scaling plot of standardized counts with TMM

Principal component plot of standardized counts with TMM Fig. 8. Principal component plot of standardized counts with TMM

This type of graph is useful for visualizing the overall effect of experimental covariances and their batch effects. When a pair of samples, under the same condition, tend to group together, a lot effect can be suspected.

From this point, the same graphs are replicated, but they show the raw counts normalized with the TMM method (Trimmed Mean of M-values), (1) Robinson.

Sección 2 - DESeq2.php

In this section, we present the results of the differential expression analysis generated by the edgeR method. (1)

Result Files

The following text files contain the results of differential expression analysis for various comparisons:

The text files can be opened in Excel and contain the result of the differential expression analysis for all genes with significant counts. The meaning of each column is as follows:

  1. id: The gene ID (gene, transcript, etc.)
  2. log2FoldChange: The log fold-change between conditions being tested
  3. logCPM: Average log2-counts per million, averaged over all libraries
  4. p value: The statistical significance of the change
  5. FDR: False Discovery Rate adjusted for multiple testing with the Benjamini-Hochberg procedure

The last columns correspond to:

  1. raw counts: Raw counts for all samples
  2. normalized counts: Normalized counts for all samples
  3. Regulation: Gene regulation, indicating how and in which condition the gene was expressed

Files

pval files

logFC files

Abundance files

Subgroup Analysis

Additional analysis files:

These “TOP.txt” files represent subgroup analyses and contain results only for differentially expressed genes based on the established cutoff parameters.

The following files have genes that it was reported as differential expressed for more than one method,(EdgeR + other)

Intersect files

Gene Expression Plots

In the context of RNA-Seq analysis, MDS plot shows variation among RNA-seq samples, with the distance between sample labels indicating dissimilarity. When the experiment is well controlled, the greatest sources of variation should correspond to the treatments/groups of interest.

The Smear plot visualizes the results of a DE analysis, showing the log-fold change against log-counts per million. Differentially expressed features are highlighted in red.

The Volcano plot summarizes both fold-change and p-values. It is a scatter-plot of the negative log10-transformed p-values against the log2 fold change. Features declared as differentially expressed are highlighted in red.

Imagen 1{:target=“_blank”}

Imagen 2{:target=“_blank”}

Imagen 3{:target=“_blank”}

Imagen 4{:target=“_blank”}

Imagen 5{:target=“_blank”}

Imagen 6{:target=“_blank”}

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Sección 3 - Edge.php

edgeR Results

Diferential Expression by DESeq2. (1)

The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of e ach column is described below.

  1. id : The first column is the id(gene, transcript, etc.)
  2. baseMean: Mean normalised counts, averaged over all samples from the two conditions
  3. log2FoldChange : logaritmo de la magnitud de cambio entre el par de condiciones, en la tercer columna
  4. lfcSE: Standard errors of logarithm fold change
  5. Stat: test statistics
  6. pvalue: p value for the statistical significance of this change
  7. padj: p value adjusted for multiple testing with the Benjamini-Hochberg procedure The last columns correspond to:
  8. raw counts: Raw counts for all sample
  9. normalized counts: Normalized counts for all samples
  10. Regulation : gene regulation, indicating how and in which condition the genes was expressed

Files

pval flies

logFC files

Abundances files

The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters).

The following files have genes that it was reported as differential expressed for more than one method (DESeq2 + other)

Graficas

plotPCA.pdfThis type of plot is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).

plotMA.pdf, This plot represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014). References:

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Sección 4 - limma.php

limma Results

In this section the differential expression results generated by the limma method are shown.(1)

The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :

id : The first column is the id(gene, transcript, etc.), log2FoldChange : The log fold-change between conditions being tested AveExpr: Average log2 expression t: the t-statistic used to assess differential expression P.Value: The p-value for differential expression; this value is not adjusted for multiple testing adj.P.Val :The p-value adjusted for multiple testing B: The B-statistic is the log-odds that the gene is differentially expressed The last columns correspond to: raw counts: Raw counts for all sample normalized counts: Normalized counts for all samples Regulation : gene regulation, indicating how and in which condition the genes was expressed

Files

pval files

logFC files

The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters). The following files have genes that it was reported as differential expressed for more than one method,(limma + other)

Abundance files

Intersect files

Graph PlotPCA

the graph plotPCA.pdf is useful for visualizing the overall effect of experimental covariates and batch effects (Love MI 2014).

In plot plotMA.pdf it represents each gene with a dot. The x axis is the average expression over the mean of normalized counts, the y axis is the log2 fold change between conditions. Features declared as differentially expressed are highlighted in red (Gonzalez 2014).

Click to enlarge the image

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Sección 5 - NOISeq.php

NOISeq Results

In this section the differential expression results generated by the NOISeq method are shown. (1)

The following text files (can be opened in Excel) they contains the result of differential expression analysis for all genes with significant counts. The meaning of each column is described below. :

  1. id : The first column is the id(gene, transcript, etc.)
  2. Cond1_mean: The second column is the mean of the biological replicates of first condition
  3. Cond2_mean: the thirds column is the mean of the biological replicates of secondth condition
  4. theta: the fourth column is a Differential expression statistics
  5. Prop: the fifth column show Probability of differential expression
  6. log2FoldChange : The log fold-change between conditions being tested The last columns correspond to:
  7. raw counts: Raw counts for all sample
  8. normalized counts: Normalized counts for all samples
  9. DE: gene regulation, indicating how and in which condition the genes was expressed

pval files

logFC files

Abundance files

Subgrupos

The following files with TOP.txt ending, it are subgroup of previos files, and it containing DE results only of the differentially expressed genes (considering the established cutoff parameters).

Differential expressed

The following files have genes that it was reported as differential expressed for more than one method,(NOISeq + others)

Graphs

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Sección 6 - VennDiagram.php

Results Integration

Once the results of the differential expression analyses has been obtained (by the different selected methods), these results are compared, by mean of Venn, bar and correlograms plots, in order to see how the different methods agree on the final list of differential expressed genes

In the process of integration of results, different files are obtained. These files contain information related to the differentially expressed genes found at the intersection of all methods. The description of the files is as follows:

Files with _table.txt ending: File of binary values, which indicate for each gene, the methods that report it as differentially expressed. In the last column of the file, a description of the gene regulation can be found, where is indicated how and in which condition the genes was expressed.

Files with _IntersectSummary.txt ending: Contains the summary of the number of DE genes in all possible logical relationships between the different methods

Files with _Intesrsect_TOP_IDs.txt ending: Table that contains the ID of those genes that are at the intersection of the DE genes obtained by all the different selected methods.

Files with _logFCTable.txt ending: Table containing the logFC values of the DE genes reported for all methods

Files with _AbundanceTable.txt ending: Table containing the raw and normalized counts of all samples of the DE genes reported for all methods

Files with _PvalTable.txt ending: Table containing the padjust/FDR values of the DE genes reported for all methods

Files

MatrixWeight files

PvalTable files

IntersectTopIDs files

UnionTop_IDs files

logFCTable files

IntersectSummary files

AbundanceTable files

Diagramas de Venn

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Graficos

The Venn diagram shows the intersection of the differentially expressed genes reported by each of the selected methods. Further analysis could be considered only in the list of genes that were identified as differentials expressed by more than one method. (In the center of the graph)

Files

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Upset files

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IntersectHeathmap files

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UnionHeathMaps files

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Presentacion de las Pre-Clasificaciones

Primera Clasificacion: pEhEx

##          GenId     CDC5_1    CDC5_2     CDC5_3    pEhEx_1    pEhEx_2    pEhEx_3
## 1  EHI_000130A  72.594848  40.99764  280.54541  169.92967  411.34855  466.86626
## 2  EHI_000140A 111.027415 145.24877  643.77044  342.73950  324.16054   37.85402
## 3  EHI_000240A 876.831894 752.01378 1122.88829 1140.54489 1837.65493 2071.45616
## 4  EHI_000250A 492.506226 510.71341  232.49229  315.37794  310.74700  642.81736
## 5  EHI_000260A  12.810856 107.76521    2.11999  105.84602   63.71431   49.07003
## 6  EHI_000280A  58.360564  42.16900  171.71923   53.28303   79.36344   60.28603
## 7  EHI_000290A  17.081141  26.94130   14.13327   18.00102   13.41354   79.21304
## 8  EHI_000300A  49.819994  70.28166   48.05312  129.60737  109.54391   30.84402
## 9  EHI_000410A  14.234284  19.91314   52.29310   27.36156   23.47369   63.79104
## 10 EHI_000430A   9.963999  25.76994    2.11999   25.20143   13.41354   10.51501
##         GenId    CDC5_1    CDC5_2     CDC5_3    pEhEx_1    pEhEx_2    pEhEx_3
## 1 EHI_000130A  72.59485  40.99764  280.54541  169.92967  411.34855  466.86626
## 2 EHI_000140A 111.02742 145.24877  643.77044  342.73950  324.16054   37.85402
## 3 EHI_000240A 876.83189 752.01378 1122.88829 1140.54489 1837.65493 2071.45616
## 4 EHI_000250A 492.50623 510.71341  232.49229  315.37794  310.74700  642.81736
## 5 EHI_000260A  12.81086 107.76521    2.11999  105.84602   63.71431   49.07003
## 6 EHI_000280A  58.36056  42.16900  171.71923   53.28303   79.36344   60.28603

Log-Normalización

##         GenId    CDC5_1    CDC5_2     CDC5_3    pEhEx_1    pEhEx_2    pEhEx_3
## 1 EHI_000130A  72.59485  40.99764  280.54541  169.92967  411.34855  466.86626
## 2 EHI_000140A 111.02742 145.24877  643.77044  342.73950  324.16054   37.85402
## 3 EHI_000240A 876.83189 752.01378 1122.88829 1140.54489 1837.65493 2071.45616
## 4 EHI_000250A 492.50623 510.71341  232.49229  315.37794  310.74700  642.81736
## 5 EHI_000260A  12.81086 107.76521    2.11999  105.84602   63.71431   49.07003
## 6 EHI_000280A  58.36056  42.16900  171.71923   53.28303   79.36344   60.28603
##   log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1    7.417259    8.687721    8.869952          6.201533          5.392236
## 2    8.425172    8.345008    5.279992          6.807708          7.192281
## 3   10.156772   10.844435   11.017126          9.777801          9.556532
## 4    8.305505    8.284232    9.330508          8.946924          8.999192
## 5    6.739389    6.016013    5.645875          3.787731          6.765073
## 6    5.762429    6.328467    5.937486          5.891433          5.431924
##   log2samplevsCDC53
## 1          8.137224
## 2          9.332642
## 3         10.134283
## 4          7.867231
## 5          1.641542
## 6          7.432285
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
## Loading required package: MASS
## 
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
## 
##     select
## Loading required package: survival
##         GenId    CDC5_1    CDC5_2     CDC5_3    pEhEx_1    pEhEx_2    pEhEx_3
## 1 EHI_000130A  72.59485  40.99764  280.54541  169.92967  411.34855  466.86626
## 2 EHI_000140A 111.02742 145.24877  643.77044  342.73950  324.16054   37.85402
## 3 EHI_000240A 876.83189 752.01378 1122.88829 1140.54489 1837.65493 2071.45616
## 4 EHI_000250A 492.50623 510.71341  232.49229  315.37794  310.74700  642.81736
## 5 EHI_000260A  12.81086 107.76521    2.11999  105.84602   63.71431   49.07003
## 6 EHI_000280A  58.36056  42.16900  171.71923   53.28303   79.36344   60.28603
##   log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1    7.417259    8.687721    8.869952          6.201533          5.392236
## 2    8.425172    8.345008    5.279992          6.807708          7.192281
## 3   10.156772   10.844435   11.017126          9.777801          9.556532
## 4    8.305505    8.284232    9.330508          8.946924          8.999192
## 5    6.739389    6.016013    5.645875          3.787731          6.765073
## 6    5.762429    6.328467    5.937486          5.891433          5.431924
##   log2samplevsCDC53
## 1          8.137224
## 2          9.332642
## 3         10.134283
## 4          7.867231
## 5          1.641542
## 6          7.432285

Muestra 1

## [1] 6.30237
## [1] 2.868113
## [1]  7.417259  8.425172 10.156772  8.305505  6.739389
##     GenId               CDC5_1              CDC5_2              CDC5_3        
##  Length:4772        Min.   :     0.00   Min.   :     0.00   Min.   :     0.0  
##  Class :character   1st Qu.:    17.08   1st Qu.:    17.57   1st Qu.:    16.3  
##  Mode  :character   Median :    45.55   Median :    49.20   Median :    44.5  
##                     Mean   :  1749.28   Mean   :  1748.01   Mean   :  1980.0  
##                     3rd Qu.:   196.79   3rd Qu.:   208.50   3rd Qu.:   177.4  
##                     Max.   :270953.87   Max.   :270338.41   Max.   :481876.0  
##     pEhEx_1            pEhEx_2             pEhEx_3          log2sample1    
##  Min.   :     0.0   Min.   :     0.00   Min.   :     0.0   Min.   : 0.000  
##  1st Qu.:    18.0   1st Qu.:    15.65   1st Qu.:    15.4   1st Qu.: 4.248  
##  Median :    50.4   Median :    49.18   Median :    54.0   Median : 5.684  
##  Mean   :  1395.0   Mean   :  1717.64   Mean   :  1909.2   Mean   : 6.302  
##  3rd Qu.:   208.1   3rd Qu.:   223.84   3rd Qu.:   242.0   3rd Qu.: 7.708  
##  Max.   :207266.7   Max.   :265749.05   Max.   :707261.7   Max.   :17.661  
##   log2sample2      log2sample3     log2samplevsCDC51 log2samplevsCDC52
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.000    Min.   : 0.000   
##  1st Qu.: 4.057   1st Qu.: 4.038   1st Qu.: 4.176    1st Qu.: 4.215   
##  Median : 5.649   Median : 5.781   Median : 5.541    Median : 5.650   
##  Mean   : 6.237   Mean   : 6.186   Mean   : 6.244    Mean   : 6.270   
##  3rd Qu.: 7.813   3rd Qu.: 7.925   3rd Qu.: 7.628    3rd Qu.: 7.711   
##  Max.   :18.020   Max.   :19.432   Max.   :18.048    Max.   :18.044   
##  log2samplevsCDC53
##  Min.   : 0.000   
##  1st Qu.: 4.109   
##  Median : 5.508   
##  Mean   : 6.114   
##  3rd Qu.: 7.479   
##  Max.   :18.878

## [1] 6.30237
## [1] 2.868113
## [1]  0.3887185  0.7401387  1.3438805  0.6984156  0.1523717 -0.1882564

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -9.053474e-17 0.01447452
## sd    9.998952e-01 0.01023499
## Loglikelihood:  -6770.675   AIC:  13545.35   BIC:  13558.29 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Cálculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8713278 0.8126272
## 70 -0.9260903 0.9617987
## 75 -0.9875514 1.1301158
## 80 -1.0213487 1.3734820
## 85 -1.0966280 1.6988689
## 90 -1.1851905 2.1819106
## 95 -1.3565981 2.6079805
## 99 -1.9245847 3.1666752

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra 2

## [1] 6.237216
## [1] 3.103083
## [1]  8.687721  8.345008 10.844435  8.284232  6.016013

Log-normalizacion

## [1] 6.237216
## [1] 3.103083
## [1]  0.78969998  0.67925752  1.48472313  0.65967167 -0.07128491  0.02940674

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -1.260395e-16 0.01447452
## sd    9.998952e-01 0.01023499
## Loglikelihood:  -6770.675   AIC:  13545.35   BIC:  13558.29 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

Calculo de Cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8926336 0.8466541
## 70 -0.9421687 0.9875692
## 75 -0.9976178 1.1428700
## 80 -1.0605870 1.3752461
## 85 -1.1334411 1.6895575
## 90 -1.2198720 2.1202686
## 95 -1.4640882 2.5136403
## 99 -2.0100061 3.0865078

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra 3

## [1] 6.186357
## [1] 3.171257
## [1]  8.869952  5.279992 11.017126  9.330508  5.645875

## [1] 6.186357
## [1] 3.171257
## [1]  0.84622446 -0.28580629  1.52329778  0.99145245 -0.17043143 -0.07847701

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 2.945427e-17 0.01447452
## sd   9.998952e-01 0.01023499
## Loglikelihood:  -6770.675   AIC:  13545.35   BIC:  13558.29 
## Correlation matrix:
##              mean           sd
## mean 1.000000e+00 1.684231e-11
## sd   1.684231e-11 1.000000e+00

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Calculo de Cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8676459 0.8379706
## 70 -0.9660265 0.9701830
## 75 -1.0458574 1.1161493
## 80 -1.1427428 1.3265284
## 85 -1.2660057 1.6213606
## 90 -1.4356115 2.0178765
## 95 -1.7090932 2.4795401
## 99 -1.9507587 3.0829063

Creación de histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC51

## [1] 6.244372
## [1] 2.880412
## [1] 6.201533 6.807708 9.777801 8.946924 3.787731

## [1] 6.244372
## [1] 2.880412
## [1] -0.01487273  0.19557464  1.22670936  0.93825188 -0.85287855 -0.12253093

Primer histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 6.276949e-17 0.01447452
## sd   9.998952e-01 0.01023499
## Loglikelihood:  -6770.675   AIC:  13545.35   BIC:  13558.29 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Calculo de Cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8528786 0.8216616
## 70 -0.9073590 0.9496778
## 75 -0.9073590 1.1007491
## 80 -0.9684970 1.3308952
## 85 -1.0381490 1.6867728
## 90 -1.1190762 2.1306178
## 95 -1.2156475 2.6928135
## 99 -2.1678747 3.3316192

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC52

## [1] 6.269726
## [1] 2.952289
## [1] 5.392236 7.192281 9.556532 8.999192 6.765073

Primer Histograma

## [1] 6.269726
## [1] 2.952289
## [1] -0.2972235  0.3124880  1.1133080  0.9245256  0.1677843 -0.2837806

Segundo Histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -3.587454e-18 0.01447452
## sd    9.998952e-01 0.01023499
## Loglikelihood:  -6770.675   AIC:  13545.35   BIC:  13558.29 
## Correlation matrix:
##              mean           sd
## mean 1.000000e+00 1.684231e-11
## sd   1.684231e-11 1.000000e+00

Cálculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8380866 0.7932340
## 70 -0.8811551 0.9359679
## 75 -0.9283894 1.1117853
## 80 -1.0392501 1.3412158
## 85 -1.1058050 1.6629743
## 90 -1.1828753 2.0953140
## 95 -1.3871587 2.6382746
## 99 -2.1236833 3.2437321

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC53

## [1] 6.11433
## [1] 2.904448
## [1]  8.137224  9.332642 10.134283  7.867231  1.641542

** Primer histograma**

## [1] 6.11433
## [1] 2.904448
## [1]  0.6964814  1.1080632  1.3840678  0.6035230 -1.5399785  0.4537713

Segundo histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -9.490135e-17 0.01447452
## sd    9.998952e-01 0.01023499
## Loglikelihood:  -6770.675   AIC:  13545.35   BIC:  13558.29 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

Calculo de Cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8305939 0.8018512
## 70 -0.8877013 0.9412276
## 75 -0.9522378 1.1212912
## 80 -1.0264291 1.3220406
## 85 -1.1638210 1.5939827
## 90 -1.2824356 2.0684839
## 95 -1.4385679 2.6057307
## 99 -1.8396440 3.5572717

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Segunda Clasificacion: pEhExvsCmasM

## # A tibble: 10 × 7
##    GenId       CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
##    <chr>        <dbl>  <dbl>  <dbl>   <dbl>   <dbl>   <dbl>
##  1 EHI_000130A   45.3  108.   129.    180.    446.    516. 
##  2 EHI_000140A   66.0  318.   257.    363.    352.     41.8
##  3 EHI_000240A  701.  1282.   877.   1209.   1993.   2290. 
##  4 EHI_000250A  707.   430.   389.    334.    337.    711. 
##  5 EHI_000260A   94.5  109.    35.2   112.     69.1    54.2
##  6 EHI_000280A   58.3   50.5   80.8    56.5    86.1    66.6
##  7 EHI_000290A   27.2   14.9   23.9    19.1    14.5    87.6
##  8 EHI_000300A   60.9  143.   111.    137.    119.     34.1
##  9 EHI_000410A   15.5   21.8   23.2    29.0    25.5    70.5
## 10 EHI_000430A   27.2   27.5   22.4    26.7    14.5    11.6
## # A tibble: 6 × 7
##   GenId       CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
##   <chr>        <dbl>  <dbl>  <dbl>   <dbl>   <dbl>   <dbl>
## 1 EHI_000130A   45.3  108.   129.    180.    446.    516. 
## 2 EHI_000140A   66.0  318.   257.    363.    352.     41.8
## 3 EHI_000240A  701.  1282.   877.   1209.   1993.   2290. 
## 4 EHI_000250A  707.   430.   389.    334.    337.    711. 
## 5 EHI_000260A   94.5  109.    35.2   112.     69.1    54.2
## 6 EHI_000280A   58.3   50.5   80.8    56.5    86.1    66.6

Log-Normalización

##         GenId    CDC5_1     CDC5_2    CDC5_3    pEhEx_1    pEhEx_2    pEhEx_3
## 1 EHI_000130A  45.32378  107.82734 128.66351  180.07428  446.15729  516.14317
## 2 EHI_000140A  66.04322  317.74653 257.32703  363.20067  351.59134   41.84945
## 3 EHI_000240A 700.57610 1282.45711 877.45524 1208.63415 1993.15918 2290.09468
## 4 EHI_000250A 707.05093  430.16227 388.98271  334.20566  337.04273  710.66559
## 5 EHI_000260A  94.53245  108.97444  35.15805  112.16491   69.10588   54.24928
## 6 EHI_000280A  58.27343   50.47237  80.78872   56.46397   86.07926   66.64912
##   log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1    7.500438    8.804639    9.014420          5.533681          6.765897
## 2    8.508590    8.461853    5.421205          6.067020          8.316266
## 3   10.240355   10.961565   11.161821          9.454456         10.325819
## 4    8.388903    8.401062    9.475056          9.467709          8.752087
## 5    6.822283    6.131464    5.787884          6.577919          6.781024
## 6    5.844586    6.444257    6.079999          5.889314          5.685726
##   log2samplevsCDC53
## 1          7.018629
## 2          8.013055
## 3          9.778825
## 4          8.607266
## 5          5.176245
## 6          6.353830
##         GenId    CDC5_1     CDC5_2    CDC5_3    pEhEx_1    pEhEx_2    pEhEx_3
## 1 EHI_000130A  45.32378  107.82734 128.66351  180.07428  446.15729  516.14317
## 2 EHI_000140A  66.04322  317.74653 257.32703  363.20067  351.59134   41.84945
## 3 EHI_000240A 700.57610 1282.45711 877.45524 1208.63415 1993.15918 2290.09468
## 4 EHI_000250A 707.05093  430.16227 388.98271  334.20566  337.04273  710.66559
## 5 EHI_000260A  94.53245  108.97444  35.15805  112.16491   69.10588   54.24928
## 6 EHI_000280A  58.27343   50.47237  80.78872   56.46397   86.07926   66.64912
##   log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1    7.500438    8.804639    9.014420          5.533681          6.765897
## 2    8.508590    8.461853    5.421205          6.067020          8.316266
## 3   10.240355   10.961565   11.161821          9.454456         10.325819
## 4    8.388903    8.401062    9.475056          9.467709          8.752087
## 5    6.822283    6.131464    5.787884          6.577919          6.781024
## 6    5.844586    6.444257    6.079999          5.889314          5.685726
##   log2samplevsCDC53
## 1          7.018629
## 2          8.013055
## 3          9.778825
## 4          8.607266
## 5          5.176245
## 6          6.353830

Muestra 1

## [1] 6.460339
## [1] 2.834041
## [1]  7.500438  8.508590 10.240355  8.388903  6.822283
##     GenId               CDC5_1             CDC5_2              CDC5_3        
##  Length:4691        Min.   :     0.0   Min.   :     0.00   Min.   :     0.0  
##  Class :character   1st Qu.:    19.4   1st Qu.:    19.50   1st Qu.:    20.9  
##  Mode  :character   Median :    50.5   Median :    56.21   Median :    54.6  
##                     Mean   :  1930.9   Mean   :  1790.28   Mean   :  2024.1  
##                     3rd Qu.:   209.8   3rd Qu.:   248.92   3rd Qu.:   223.3  
##                     Max.   :405707.4   Max.   :282737.06   Max.   :384267.8  
##     pEhEx_1             pEhEx_2             pEhEx_3          log2sample1    
##  Min.   :     0.00   Min.   :     0.00   Min.   :     0.0   Min.   : 0.000  
##  1st Qu.:    19.84   1st Qu.:    18.19   1st Qu.:    17.0   1st Qu.: 4.381  
##  Median :    55.70   Median :    56.98   Median :    60.4   Median : 5.825  
##  Mean   :  1503.81   Mean   :  1895.10   Mean   :  2146.4   Mean   : 6.460  
##  3rd Qu.:   227.38   3rd Qu.:   250.96   3rd Qu.:   272.4   3rd Qu.: 7.835  
##  Max.   :219640.26   Max.   :288237.01   Max.   :781911.9   Max.   :17.745  
##   log2sample2      log2sample3     log2samplevsCDC51 log2samplevsCDC52
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.000    Min.   : 0.000   
##  1st Qu.: 4.262   1st Qu.: 4.174   1st Qu.: 4.352    1st Qu.: 4.358   
##  Median : 5.858   Median : 5.941   Median : 5.687    Median : 5.838   
##  Mean   : 6.424   Mean   : 6.345   Mean   : 6.361    Mean   : 6.456   
##  3rd Qu.: 7.977   3rd Qu.: 8.095   3rd Qu.: 7.720    3rd Qu.: 7.965   
##  Max.   :18.137   Max.   :19.577   Max.   :18.630    Max.   :18.109   
##  log2samplevsCDC53
##  Min.   : 0.000   
##  1st Qu.: 4.456   
##  Median : 5.797   
##  Mean   : 6.531   
##  3rd Qu.: 7.809   
##  Max.   :18.552

## [1] 6.460339
## [1] 2.834041
## [1]  0.3670021  0.7227316  1.3337904  0.6804997  0.1277131 -0.2172704

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 3.118970e-17 0.01459893
## sd   9.998934e-01 0.01032296
## Loglikelihood:  -6655.741   AIC:  13315.48   BIC:  13328.39 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Cálculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8599158 0.8169091
## 70 -0.9371527 0.9617338
## 75 -0.9960331 1.1293972
## 80 -1.0282412 1.3839029
## 85 -1.0995019 1.7041875
## 90 -1.1392596 2.1899810
## 95 -1.2814439 2.6187913
## 99 -1.6734604 3.1869302

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra 2

## [1] 6.424318
## [1] 3.078114
## [1]  8.804639  8.461853 10.961565  8.401062  6.131464

Log-normalizacion

## [1] 6.424318
## [1] 3.078114
## [1]  0.773304903  0.661942766  1.474034750  0.642193202 -0.095140848
## [6]  0.006477743

Ajuste de modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -7.845328e-17 0.01459893
## sd    9.998934e-01 0.01032296
## Loglikelihood:  -6655.741   AIC:  13315.48   BIC:  13328.39 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

Calculo de Cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8804743 0.8477586
## 70 -0.9259047 0.9845324
## 75 -0.9762161 1.1416416
## 80 -1.0325848 1.3729846
## 85 -1.1709305 1.7147626
## 90 -1.2592113 2.1352442
## 95 -1.5101169 2.5267006
## 99 -2.0870956 3.0982665

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra 3

## [1] 6.345251
## [1] 3.202711
## [1]  9.014420  5.421205 11.161821  9.475056  5.787884

## [1] 6.345251
## [1] 3.202711
## [1]  0.83340927 -0.28851998  1.50390416  0.97723608 -0.17402974 -0.08282095

Ajustando Modelos

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 2.404120e-17 0.01459893
## sd   9.998934e-01 0.01032296
## Loglikelihood:  -6655.741   AIC:  13315.48   BIC:  13328.39 
## Correlation matrix:
##              mean           sd
## mean 1.000000e+00 1.713307e-11
## sd   1.713307e-11 1.000000e+00

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Calculo de Cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8675316 0.8402759
## 70 -0.9659157 0.9685687
## 75 -1.0459211 1.1104543
## 80 -1.1432722 1.3204414
## 85 -1.2676335 1.6079001
## 90 -1.4400018 1.9997962
## 95 -1.7227405 2.4584046
## 99 -1.9812123 3.0490511

Creación de histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC51

## [1] 6.360533
## [1] 2.885557
## [1] 5.533681 6.067020 9.454456 9.467709 6.577919

## [1] 6.360533
## [1] 2.885557
## [1] -0.28654857 -0.10171821  1.07220996  1.07680301  0.07533583 -0.16330282

Primer histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 4.727431e-17 0.01459893
## sd   9.998934e-01 0.01032296
## Loglikelihood:  -6655.741   AIC:  13315.48   BIC:  13328.39 
## Correlation matrix:
##              mean           sd
## mean 1.000000e+00 3.426614e-11
## sd   3.426614e-11 1.000000e+00

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Cálculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8422310 0.8070408
## 70 -0.8866144 0.9258394
## 75 -0.9353250 1.1082535
## 80 -0.9892988 1.3411647
## 85 -1.0498123 1.6503084
## 90 -1.1186718 2.1232202
## 95 -1.2936703 2.7259168
## 99 -2.2042655 3.3471237

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC52

## [1] 6.456006
## [1] 2.961412
## [1]  6.765897  8.316266 10.325819  8.752087  6.781024

Primer Histograma

## [1] 6.456006
## [1] 2.961412
## [1]  0.1046430  0.6281664  1.3067458  0.7753331  0.1097511 -0.2601055

Segundo Histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -1.572806e-17 0.01459893
## sd    9.998934e-01 0.01032296
## Loglikelihood:  -6655.741   AIC:  13315.48   BIC:  13328.39 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

Cálculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8684632 0.8127421
## 70 -0.9078618 0.9668311
## 75 -0.9977360 1.1263553
## 80 -1.0497676 1.3726202
## 85 -1.1080283 1.6925494
## 90 -1.1742151 2.1332703
## 95 -1.3417825 2.5710200
## 99 -2.1800429 3.1603637

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC53

## [1] 6.53086
## [1] 2.83016
## [1] 7.018629 8.013055 9.778825 8.607266 5.176245

Primer histograma

## [1] 6.53086
## [1] 2.83016
## [1]  0.17234655  0.52371392  1.14762562  0.73367078 -0.47863552 -0.06255138

Segundo histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 4.356074e-17 0.01459893
## sd   9.998934e-01 0.01032296
## Loglikelihood:  -6655.741   AIC:  13315.48   BIC:  13328.39 
## Correlation matrix:
##               mean            sd
## mean  1.000000e+00 -3.426614e-11
## sd   -3.426614e-11  1.000000e+00

Calculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8498210 0.8013314
## 70 -0.8954944 0.9236834
## 75 -0.9456663 1.1144463
## 80 -1.0013208 1.3533967
## 85 -1.0316061 1.7113380
## 90 -1.0981755 2.2519051
## 95 -1.2178539 2.7028284
## 99 -1.5625210 3.3998336

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Tercera Clasificacion: pEhExvsEhMyb10

## # A tibble: 10 × 7
##    GenId       CDC5_1 CDC5_2   CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
##    <chr>        <dbl>  <dbl>    <dbl>   <dbl>   <dbl>   <dbl>
##  1 EHI_000130A   69.9   61.2   502.     140.    346.   366.  
##  2 EHI_000140A  216.    28.8    65.9    281.    272.    29.6 
##  3 EHI_000240A  851.   489.  12365.     936.   1544.  1622.  
##  4 EHI_000250A  413.   616.   1844.     259.    261.   503.  
##  5 EHI_000260A   81.6   77.4   517.      86.9    53.5   38.4 
##  6 EHI_000280A   35.9   48.6    59.6     43.7    66.7   47.2 
##  7 EHI_000290A   12.6   23.4    47.0     14.8    11.3   62.0 
##  8 EHI_000300A  104.    68.4     9.41   106.     92.0   24.2 
##  9 EHI_000410A   17.0   10.8   144.      22.5    19.7   50.0 
## 10 EHI_000430A   18.8   19.8    25.1     20.7    11.3    8.24
## # A tibble: 6 × 7
##   GenId       CDC5_1 CDC5_2  CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
##   <chr>        <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 EHI_000130A   69.9   61.2   502.    140.    346.    366. 
## 2 EHI_000140A  216.    28.8    65.9   281.    272.     29.6
## 3 EHI_000240A  851.   489.  12365.    936.   1544.   1622. 
## 4 EHI_000250A  413.   616.   1844.    259.    261.    503. 
## 5 EHI_000260A   81.6   77.4   517.     86.9    53.5    38.4
## 6 EHI_000280A   35.9   48.6    59.6    43.7    66.7    47.2

Log-Normalización

##         GenId    CDC5_1    CDC5_2      CDC5_3   pEhEx_1    pEhEx_2    pEhEx_3
## 1 EHI_000130A  69.92805  61.19384   501.73716 139.50869  345.55778  365.63668
## 2 EHI_000140A 216.05975  28.79710    65.85300 281.38193  272.31456   29.64622
## 3 EHI_000240A 850.79131 488.65084 12364.68511 936.36340 1543.74183 1622.30687
## 4 EHI_000250A 413.29272 616.43798  1843.88406 258.91867  261.04637  503.43668
## 5 EHI_000260A  81.58273  77.39221   517.41645  86.89736   53.52390   38.43028
## 6 EHI_000280A  35.86054  48.59511    59.58129  43.74425   66.67012   47.21435
##   log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1    7.134516    8.436952    8.518207          6.148284          5.958700
## 2    8.141504    8.094418    4.937637          7.761948          4.897100
## 3    9.872465   10.593150   10.664720          9.734356          8.935610
## 4    8.021916    8.033678    8.978529          8.694507          9.270150
## 5    6.457748    5.768817    5.301232          6.367768          6.292638
## 6    5.483630    6.080447    5.591391          5.204005          5.632126
##   log2samplevsCDC53
## 1          8.973661
## 2          6.062920
## 3         13.594055
## 4         10.849314
## 5          9.017968
## 6          5.920800
##         GenId    CDC5_1    CDC5_2      CDC5_3   pEhEx_1    pEhEx_2    pEhEx_3
## 1 EHI_000130A  69.92805  61.19384   501.73716 139.50869  345.55778  365.63668
## 2 EHI_000140A 216.05975  28.79710    65.85300 281.38193  272.31456   29.64622
## 3 EHI_000240A 850.79131 488.65084 12364.68511 936.36340 1543.74183 1622.30687
## 4 EHI_000250A 413.29272 616.43798  1843.88406 258.91867  261.04637  503.43668
## 5 EHI_000260A  81.58273  77.39221   517.41645  86.89736   53.52390   38.43028
## 6 EHI_000280A  35.86054  48.59511    59.58129  43.74425   66.67012   47.21435
##   log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1    7.134516    8.436952    8.518207          6.148284          5.958700
## 2    8.141504    8.094418    4.937637          7.761948          4.897100
## 3    9.872465   10.593150   10.664720          9.734356          8.935610
## 4    8.021916    8.033678    8.978529          8.694507          9.270150
## 5    6.457748    5.768817    5.301232          6.367768          6.292638
## 6    5.483630    6.080447    5.591391          5.204005          5.632126
##   log2samplevsCDC53
## 1          8.973661
## 2          6.062920
## 3         13.594055
## 4         10.849314
## 5          9.017968
## 6          5.920800

Muestra 1

## [1] 6.089174
## [1] 2.844545
## [1] 7.134516 8.141504 9.872465 8.021916 6.457748
##     GenId               CDC5_1              CDC5_2             CDC5_3         
##  Length:4687        Min.   :     0.00   Min.   :     0.0   Min.   :      0.0  
##  Class :character   1st Qu.:    14.34   1st Qu.:    16.2   1st Qu.:     12.5  
##  Mode  :character   Median :    41.24   Median :    41.4   Median :     50.2  
##                     Mean   :  1568.76   Mean   :  1496.5   Mean   :   4142.0  
##                     3rd Qu.:   189.61   3rd Qu.:   167.4   3rd Qu.:    239.9  
##                     Max.   :247688.75   Max.   :404961.1   Max.   :2325768.1  
##     pEhEx_1             pEhEx_2             pEhEx_3          log2sample1    
##  Min.   :     0.00   Min.   :     0.00   Min.   :     0.0   Min.   : 0.000  
##  1st Qu.:    15.37   1st Qu.:    14.09   1st Qu.:    12.6   1st Qu.: 4.033  
##  Median :    43.15   Median :    44.13   Median :    43.9   Median : 5.464  
##  Mean   :  1165.87   Mean   :  1467.37   Mean   :  1522.5   Mean   : 6.089  
##  3rd Qu.:   176.16   3rd Qu.:   194.38   3rd Qu.:   193.2   3rd Qu.: 7.469  
##  Max.   :170161.59   Max.   :223245.35   Max.   :553907.7   Max.   :17.377  
##   log2sample2      log2sample3     log2samplevsCDC51 log2samplevsCDC52
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.000    Min.   : 0.000   
##  1st Qu.: 3.915   1st Qu.: 3.768   1st Qu.: 3.940    1st Qu.: 4.104   
##  Median : 5.496   Median : 5.489   Median : 5.401    Median : 5.406   
##  Mean   : 6.054   Mean   : 5.945   Mean   : 6.079    Mean   : 6.086   
##  3rd Qu.: 7.610   3rd Qu.: 7.602   3rd Qu.: 7.574    3rd Qu.: 7.396   
##  Max.   :17.768   Max.   :19.079   Max.   :17.918    Max.   :18.627   
##  log2samplevsCDC53
##  Min.   : 0.000   
##  1st Qu.: 3.760   
##  Median : 5.677   
##  Mean   : 6.068   
##  3rd Qu.: 7.912   
##  Max.   :21.149

## [1] 6.089174
## [1] 2.844545
## [1]  0.3674899  0.7214968  1.3300161  0.6794558  0.1295722 -0.2128788

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 3.587875e-17 0.01460516
## sd   9.998933e-01 0.01032736
## Loglikelihood:  -6650.065   AIC:  13304.13   BIC:  13317.03 
## Correlation matrix:
##               mean            sd
## mean  1.000000e+00 -3.429538e-11
## sd   -3.429538e-11  1.000000e+00

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Cálculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8467216 0.8152581
## 70 -0.9222155 0.9595311
## 75 -0.9796121 1.1276277
## 80 -1.0443431 1.3803954
## 85 -1.0800958 1.7000005
## 90 -1.1601851 2.1830409
## 95 -1.3725612 2.6106296
## 99 -1.7448623 3.1766074

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra 2

## [1] 6.054099
## [1] 3.081702
## [1]  8.436952  8.094418 10.593150  8.033678  5.768817

Log-normalizacion

## [1] 6.054099
## [1] 3.081702
## [1]  0.773226200  0.662075403  1.472903852  0.642365454 -0.092573117
## [6]  0.008549646

Ajuste de modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 1.403180e-16 0.01460516
## sd   9.998933e-01 0.01032736
## Loglikelihood:  -6650.065   AIC:  13304.13   BIC:  13317.03 
## Correlation matrix:
##              mean           sd
## mean 1.000000e+00 3.429538e-11
## sd   3.429538e-11 1.000000e+00

Calculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8686609 0.8470080
## 70 -0.9130056 0.9828038
## 75 -0.9619944 1.1403896
## 80 -1.0786897 1.3709267
## 85 -1.1501384 1.7083438
## 90 -1.2344905 2.1268598
## 95 -1.4696481 2.5248146
## 99 -1.9645312 3.0956436

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra 3

## [1] 5.944632
## [1] 3.109086
## [1]  8.518207  4.937637 10.664720  8.978529  5.301232

## [1] 5.944632
## [1] 3.109086
## [1]  0.8277593 -0.3238879  1.5181592  0.9758164 -0.2069419 -0.1136159

Ajustando Modelos

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -1.038159e-17 0.01460516
## sd    9.998933e-01 0.01032736
## Loglikelihood:  -6650.065   AIC:  13304.13   BIC:  13317.03 
## Correlation matrix:
##              mean           sd
## mean 1.000000e+00 1.714769e-11
## sd   1.714769e-11 1.000000e+00

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Calculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf   LimSup
## 65 -0.8804873 0.835306
## 70 -0.9392233 0.967202
## 75 -1.0441669 1.113353
## 80 -1.1800004 1.329308
## 85 -1.2993075 1.626575
## 90 -1.3728657 2.029023
## 95 -1.5681818 2.501518
## 99 -1.9120192 3.109798

Creación de histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC51

## [1] 6.079067
## [1] 3.007873
## [1] 6.148284 7.761948 9.734356 8.694507 6.367768

## [1] 6.079067
## [1] 3.007873
## [1]  0.02301208  0.55949211  1.21524037  0.86953116  0.09598182 -0.29092372

Primer histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 3.734449e-17 0.01460516
## sd   9.998933e-01 0.01032736
## Loglikelihood:  -6650.065   AIC:  13304.13   BIC:  13317.03 
## Correlation matrix:
##               mean            sd
## mean  1.000000e+00 -3.429538e-11
## sd   -3.429538e-11  1.000000e+00

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Calculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8769970 0.8328201
## 70 -0.9183158 0.9594611
## 75 -0.9635325 1.1254243
## 80 -1.0134598 1.3759593
## 85 -1.0691946 1.6825955
## 90 -1.2049095 2.1281103
## 95 -1.3948811 2.6187546
## 99 -2.0210515 3.1616174

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC52

## [1] 6.08571
## [1] 2.815509
## [1] 5.958700 4.897100 8.935610 9.270150 6.292638

Primer Histograma

## [1] 6.08571
## [1] 2.815509
## [1] -0.04511077 -0.42216506  1.01221485  1.13103560  0.07349607 -0.16110183

Segundo Histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 7.844575e-18 0.01460516
## sd   9.998933e-01 0.01032736
## Loglikelihood:  -6650.065   AIC:  13304.13   BIC:  13317.03 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

Cálculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8241172 0.7977969
## 70 -0.8968636 0.9352333
## 75 -0.9375162 1.0847564
## 80 -0.9816741 1.3463833
## 85 -1.0299993 1.6629255
## 90 -1.0833612 2.1783677
## 95 -1.2880076 2.7298034
## 99 -2.1614954 3.4267131

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC53

## [1] 6.068265
## [1] 3.43608
## [1]  8.973661  6.062920 13.594055 10.849314  9.017968

Primer histograma

## [1] 6.068265
## [1] 3.43608
## [1]  0.845555235 -0.001555479  2.190225383  1.391425525  0.858449916
## [6] -0.042916597

Segundo histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -1.107054e-16 0.01460516
## sd    9.998933e-01 0.01032736
## Loglikelihood:  -6650.065   AIC:  13304.13   BIC:  13317.03 
## Correlation matrix:
##              mean           sd
## mean 1.000000e+00 1.714769e-11
## sd   1.714769e-11 1.000000e+00

Calculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.7824916 0.8709604
## 70 -0.9330312 0.9884576
## 75 -1.1699608 1.1455636
## 80 -1.1699608 1.3353109
## 85 -1.1699608 1.5512574
## 90 -1.7660431 1.8682788
## 95 -1.7660431 2.3078117
## 99 -1.7660431 3.1171283

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Cuarta Clasificacion: pEhExvsU2AF84

## # A tibble: 10 × 7
##    GenId       CDC5_1 CDC5_2   CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
##    <chr>        <dbl>  <dbl>    <dbl>   <dbl>   <dbl>   <dbl>
##  1 EHI_000130A   59.6   70.4   611.     140.    347.   382.  
##  2 EHI_000140A  118.   207.     91.9    283.    273.    31.0 
##  3 EHI_000240A  689.   871.  11090.     941.   1550.  1694.  
##  4 EHI_000250A  426.   407.   1608.     260.    262.   526.  
##  5 EHI_000260A  104.   110.    422.      87.3    53.7   40.1 
##  6 EHI_000280A   40.4   35.2   108.      43.9    66.9   49.3 
##  7 EHI_000290A   15.2   19.7    44.8     14.8    11.3   64.8 
##  8 EHI_000300A   74.8   94.2     2.36   107.     92.4   25.2 
##  9 EHI_000410A   15.2   15.5   134.      22.6    19.8   52.2 
## 10 EHI_000430A   18.2   23.8    28.3     20.8    11.3    8.60
## # A tibble: 6 × 7
##   GenId       CDC5_1 CDC5_2  CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
##   <chr>        <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 EHI_000130A   59.6   70.4   611.    140.    347.    382. 
## 2 EHI_000140A  118.   207.     91.9   283.    273.     31.0
## 3 EHI_000240A  689.   871.  11090.    941.   1550.   1694. 
## 4 EHI_000250A  426.   407.   1608.    260.    262.    526. 
## 5 EHI_000260A  104.   110.    422.     87.3    53.7    40.1
## 6 EHI_000280A   40.4   35.2   108.     43.9    66.9    49.3

Log-Normalización

##         GenId    CDC5_1    CDC5_2      CDC5_3   pEhEx_1    pEhEx_2    pEhEx_3
## 1 EHI_000130A  59.61996  70.37838   610.61850 140.16143  346.92628  381.74940
## 2 EHI_000140A 118.22941 206.99523    91.94642 282.69848  273.39300   30.95265
## 3 EHI_000240A 689.16628 871.44992 11090.15226 940.74452 1549.85546 1693.79799
## 4 EHI_000250A 426.43427 406.74563  1607.88347 260.13011  262.08018  525.62192
## 5 EHI_000260A 104.08230 109.70747   422.01047  87.30394   53.73586   40.12381
## 6 EHI_000280A  40.42031  35.18919   108.44962  43.94892   66.93415   49.29497
##   log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1    7.141202    8.442638    8.580256          5.921721          6.157415
## 2    8.148215    8.100100    4.997864          6.897596          7.700407
## 3    9.879192   10.598849   10.726898          9.430800          9.768929
## 4    8.028625    8.039359    9.040624          8.739559          8.671526
## 5    6.464406    5.774415    5.361902          6.715376          6.790609
## 6    5.490215    6.086065    5.652342          5.372266          5.177487
##   log2samplevsCDC53
## 1          9.256488
## 2          6.538327
## 3         13.437122
## 4         10.651844
## 5          8.724550
## 6          6.774123
##         GenId    CDC5_1    CDC5_2      CDC5_3   pEhEx_1    pEhEx_2    pEhEx_3
## 1 EHI_000130A  59.61996  70.37838   610.61850 140.16143  346.92628  381.74940
## 2 EHI_000140A 118.22941 206.99523    91.94642 282.69848  273.39300   30.95265
## 3 EHI_000240A 689.16628 871.44992 11090.15226 940.74452 1549.85546 1693.79799
## 4 EHI_000250A 426.43427 406.74563  1607.88347 260.13011  262.08018  525.62192
## 5 EHI_000260A 104.08230 109.70747   422.01047  87.30394   53.73586   40.12381
## 6 EHI_000280A  40.42031  35.18919   108.44962  43.94892   66.93415   49.29497
##   log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1    7.141202    8.442638    8.580256          5.921721          6.157415
## 2    8.148215    8.100100    4.997864          6.897596          7.700407
## 3    9.879192   10.598849   10.726898          9.430800          9.768929
## 4    8.028625    8.039359    9.040624          8.739559          8.671526
## 5    6.464406    5.774415    5.361902          6.715376          6.790609
## 6    5.490215    6.086065    5.652342          5.372266          5.177487
##   log2samplevsCDC53
## 1          9.256488
## 2          6.538327
## 3         13.437122
## 4         10.651844
## 5          8.724550
## 6          6.774123

Muestra 1

## [1] 6.066239
## [1] 2.837457
## [1] 7.141202 8.148215 9.879192 8.028625 6.464406
##     GenId               CDC5_1              CDC5_2              CDC5_3         
##  Length:4746        Min.   :     0.00   Min.   :     0.00   Min.   :      0.0  
##  Class :character   1st Qu.:    15.16   1st Qu.:    14.49   1st Qu.:     14.1  
##  Mode  :character   Median :    41.43   Median :    42.43   Median :     49.5  
##                     Mean   :  1422.93   Mean   :  1431.84   Mean   :   3498.7  
##                     3rd Qu.:   168.75   3rd Qu.:   187.33   3rd Qu.:    237.5  
##                     Max.   :236529.55   Max.   :222485.72   Max.   :1942165.3  
##     pEhEx_1             pEhEx_2             pEhEx_3          log2sample1    
##  Min.   :     0.00   Min.   :     0.00   Min.   :     0.0   Min.   : 0.000  
##  1st Qu.:    14.85   1st Qu.:    13.20   1st Qu.:    12.6   1st Qu.: 3.986  
##  Median :    42.17   Median :    42.42   Median :    44.7   Median : 5.432  
##  Mean   :  1156.92   Mean   :  1454.96   Mean   :  1569.6   Mean   : 6.066  
##  3rd Qu.:   172.68   3rd Qu.:   191.38   3rd Qu.:   199.5   3rd Qu.: 7.440  
##  Max.   :170957.75   Max.   :224129.46   Max.   :578317.1   Max.   :17.383  
##   log2sample2      log2sample3     log2samplevsCDC51 log2samplevsCDC52
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.000    Min.   : 0.000   
##  1st Qu.: 3.828   1st Qu.: 3.767   1st Qu.: 4.014    1st Qu.: 3.953   
##  Median : 5.440   Median : 5.514   Median : 5.407    Median : 5.441   
##  Mean   : 6.019   Mean   : 5.939   Mean   : 6.074    Mean   : 6.080   
##  3rd Qu.: 7.588   3rd Qu.: 7.647   3rd Qu.: 7.407    3rd Qu.: 7.557   
##  Max.   :17.774   Max.   :19.142   Max.   :17.852    Max.   :17.763   
##  log2samplevsCDC53
##  Min.   : 0.000   
##  1st Qu.: 3.921   
##  Median : 5.658   
##  Mean   : 6.152   
##  3rd Qu.: 7.898   
##  Max.   :20.889

## [1] 6.066239
## [1] 2.837457
## [1]  0.3788472  0.7337468  1.3437920  0.6916001  0.1403251 -0.2030074

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -5.346355e-17 0.01451411
## sd    9.998946e-01 0.01026298
## Loglikelihood:  -6733.782   AIC:  13471.56   BIC:  13484.49 
## Correlation matrix:
##               mean            sd
## mean  1.000000e+00 -3.386913e-11
## sd   -3.386913e-11  1.000000e+00

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Cálculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8625714 0.8124762
## 70 -0.9422460 0.9640896
## 75 -0.9718431 1.1342860
## 80 -1.0367684 1.3790909
## 85 -1.0726303 1.7033941
## 90 -1.1529698 2.1934547
## 95 -1.3038487 2.6233257
## 99 -1.7398510 3.1888833

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra 2

## [1] 6.018835
## [1] 3.085311
## [1]  8.442638  8.100100 10.598849  8.039359  5.774415

Log-normalizacion

## [1] 6.018835
## [1] 3.085311
## [1]  0.78559421  0.67457202  1.48445747  0.65488484 -0.07922075  0.02179028

Ajuste de modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 4.519200e-17 0.01451411
## sd   9.998946e-01 0.01026298
## Loglikelihood:  -6733.782   AIC:  13471.56   BIC:  13484.49 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

Calculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8988548 0.8463966
## 70 -0.9478079 0.9891035
## 75 -1.0024917 1.1467315
## 80 -1.0644280 1.3749391
## 85 -1.1358390 1.6937524
## 90 -1.2201564 2.1241067
## 95 -1.4552922 2.5236980
## 99 -1.9508033 3.0984441

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra 3

## [1] 5.938851
## [1] 3.137257
## [1]  8.580256  4.997864 10.726898  9.040624  5.361902

## [1] 5.938851
## [1] 3.137257
## [1]  0.84194733 -0.29993956  1.52618884  0.98868937 -0.18390246 -0.09132474

Ajustando Modelos

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 1.897633e-17 0.01451411
## sd   9.998946e-01 0.01026298
## Loglikelihood:  -6733.782   AIC:  13471.56   BIC:  13484.49 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Calculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8813312 0.8377086
## 70 -0.9785407 0.9709584
## 75 -1.0570406 1.1167404
## 80 -1.1517563 1.3292508
## 85 -1.2711789 1.6268787
## 90 -1.4329274 2.0299674
## 95 -1.5417724 2.4960043
## 99 -1.8930077 3.1029256

Creación de histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC51

## [1] 6.073889
## [1] 2.856279
## [1] 5.921721 6.897596 9.430800 8.739559 6.715376

## [1] 6.073889
## [1] 2.856279
## [1] -0.05327506  0.28838461  1.17527408  0.93326643  0.22458816 -0.24564228

Primer histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -1.363897e-18 0.01451411
## sd    9.998946e-01 0.01026298
## Loglikelihood:  -6733.782   AIC:  13471.56   BIC:  13484.49 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Calculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8665460 0.8005323
## 70 -0.9105349 0.9400738
## 75 -0.9587235 1.1113009
## 80 -1.0119991 1.3461767
## 85 -1.0715638 1.6701383
## 90 -1.0715638 2.1555034
## 95 -1.3093554 2.7085444
## 99 -2.1265041 3.2907409

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC52

## [1] 6.079551
## [1] 2.958446
## [1] 6.157415 7.700407 9.768929 8.671526 6.790609

Primer Histograma

## [1] 6.079551
## [1] 2.958446
## [1]  0.02631943  0.54787417  1.24706626  0.87612726  0.24034856 -0.30491137

Segundo Histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -9.044811e-17 0.01451411
## sd    9.998946e-01 0.01026298
## Loglikelihood:  -6733.782   AIC:  13471.56   BIC:  13484.49 
## Correlation matrix:
##               mean            sd
## mean  1.000000e+00 -3.386913e-11
## sd   -3.386913e-11  1.000000e+00

Cálculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8703761 0.8136491
## 70 -0.9170047 0.9590243
## 75 -0.9685679 1.1169792
## 80 -1.0262349 1.3717093
## 85 -1.0916475 1.6775570
## 90 -1.1672140 2.1543218
## 95 -1.3663234 2.6103392
## 99 -2.0549813 3.1594798

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsCDC53

## [1] 6.151641
## [1] 3.315575
## [1]  9.256488  6.538327 13.437122 10.651844  8.724550

Primer histograma

## [1] 6.151641
## [1] 3.315575
## [1] 0.9364429 0.1166271 2.1973504 1.3572917 0.7760066 0.1877447

Segundo histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -1.080389e-16 0.01451411
## sd    9.998946e-01 0.01026298
## Loglikelihood:  -6733.782   AIC:  13471.56   BIC:  13484.49 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

Calculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8351247 0.8675257
## 70 -0.9466147 0.9977469
## 75 -0.9466147 1.1516663
## 80 -1.0968948 1.3476203
## 85 -1.3283399 1.5933747
## 90 -1.3283399 1.8884142
## 95 -1.8553769 2.3619041
## 99 -1.8553769 3.1601836

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Quinta Clasificacion: pEhExvsUmasM

## # A tibble: 10 × 7
##    GenId       UmasM_1 UmasM_2 UmasM_3 pEhEx_1 pEhEx_2 pEhEx_3
##    <chr>         <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 EHI_000130A    52.8   428.    877.    175.    432.    478. 
##  2 EHI_000140A   118.    371.     43.7   353.    341.     38.8
##  3 EHI_000240A   744.   1165.   7104.   1175.   1932.   2122. 
##  4 EHI_000250A   704.    213.   1578.    325.    327.    659. 
##  5 EHI_000260A   119.     96.2   139.    109.     67.0    50.3
##  6 EHI_000280A    57.6    59.6    34.3    54.9    83.4    61.8
##  7 EHI_000290A    26.4    19.7    70.2    18.5    14.1    81.2
##  8 EHI_000300A    80.4   115.     15.6   134.    115.     31.6
##  9 EHI_000410A    20.4    24.4   139.     28.2    24.7    65.4
## 10 EHI_000430A    31.2    35.9    14.0    26.0    14.1    10.8
## # A tibble: 6 × 7
##   GenId       UmasM_1 UmasM_2 UmasM_3 pEhEx_1 pEhEx_2 pEhEx_3
##   <chr>         <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 EHI_000130A    52.8   428.    877.    175.    432.    478. 
## 2 EHI_000140A   118.    371.     43.7   353.    341.     38.8
## 3 EHI_000240A   744.   1165.   7104.   1175.   1932.   2122. 
## 4 EHI_000250A   704.    213.   1578.    325.    327.    659. 
## 5 EHI_000260A   119.     96.2   139.    109.     67.0    50.3
## 6 EHI_000280A    57.6    59.6    34.3    54.9    83.4    61.8

Log-Normalización

##         GenId   UmasM_1    UmasM_2    UmasM_3    pEhEx_1    pEhEx_2    pEhEx_3
## 1 EHI_000130A  52.79569  428.31302  877.32975  175.05490  432.49403  478.29632
## 2 EHI_000140A 117.59041  370.70763   43.71038  353.07682  340.82410   38.78078
## 3 EHI_000240A 743.93931 1164.98430 7104.49766 1174.94473 1932.12006 2122.17063
## 4 EHI_000250A 704.34254  212.80109 1578.25690  324.89002  326.72103  658.55515
## 5 EHI_000260A 118.79031   96.23489  138.93656  109.03843   66.98956   50.27139
## 6 EHI_000280A  57.59530   59.63852   34.34387   54.89009   83.44314   61.76199
##   log2sample1 log2sample2 log2sample3 log2samplevsumasM1 log2samplevsumasM2
## 1    7.459882    8.759868    8.904774           5.749419           8.745886
## 2    8.467919    8.417110    5.314000           6.889844           8.538024
## 3   10.199605   10.916716   11.052005           9.540979          10.187333
## 4    8.348241    8.356324    9.365349           9.462180           7.740125
## 5    6.781864    6.087241    5.680082           6.904367           6.603402
## 6    5.804521    6.399908    5.971819           5.872713           5.922163
##   log2samplevsumasM3
## 1           9.778619
## 2           5.482538
## 3          12.794720
## 4          10.625030
## 5           7.128629
## 6           5.143388
##         GenId   UmasM_1    UmasM_2    UmasM_3    pEhEx_1    pEhEx_2    pEhEx_3
## 1 EHI_000130A  52.79569  428.31302  877.32975  175.05490  432.49403  478.29632
## 2 EHI_000140A 117.59041  370.70763   43.71038  353.07682  340.82410   38.78078
## 3 EHI_000240A 743.93931 1164.98430 7104.49766 1174.94473 1932.12006 2122.17063
## 4 EHI_000250A 704.34254  212.80109 1578.25690  324.89002  326.72103  658.55515
## 5 EHI_000260A 118.79031   96.23489  138.93656  109.03843   66.98956   50.27139
## 6 EHI_000280A  57.59530   59.63852   34.34387   54.89009   83.44314   61.76199
##   log2sample1 log2sample2 log2sample3 log2samplevsumasM1 log2samplevsumasM2
## 1    7.459882    8.759868    8.904774           5.749419           8.745886
## 2    8.467919    8.417110    5.314000           6.889844           8.538024
## 3   10.199605   10.916716   11.052005           9.540979          10.187333
## 4    8.348241    8.356324    9.365349           9.462180           7.740125
## 5    6.781864    6.087241    5.680082           6.904367           6.603402
## 6    5.804521    6.399908    5.971819           5.872713           5.922163
##   log2samplevsumasM3
## 1           9.778619
## 2           5.482538
## 3          12.794720
## 4          10.625030
## 5           7.128629
## 6           5.143388

Muestra 1

## [1] 6.24652
## [1] 2.884429
## [1]  7.459882  8.467919 10.199605  8.348241  6.781864
##     GenId              UmasM_1            UmasM_2             UmasM_3         
##  Length:4919        Min.   :     0.0   Min.   :     0.00   Min.   :      0.0  
##  Class :character   1st Qu.:    16.8   1st Qu.:    18.30   1st Qu.:     14.0  
##  Mode  :character   Median :    46.8   Median :    52.18   Median :     54.6  
##                     Mean   :  1915.1   Mean   :  1003.67   Mean   :   3444.8  
##                     3rd Qu.:   198.0   3rd Qu.:   193.15   3rd Qu.:    295.0  
##                     Max.   :340994.2   Max.   :145896.83   Max.   :1488475.8  
##     pEhEx_1             pEhEx_2             pEhEx_3          log2sample1    
##  Min.   :     0.00   Min.   :     0.00   Min.   :     0.0   Min.   : 0.000  
##  1st Qu.:    16.32   1st Qu.:    15.28   1st Qu.:    15.1   1st Qu.: 4.114  
##  Median :    48.96   Median :    48.19   Median :    52.4   Median : 5.643  
##  Mean   :  1394.44   Mean   :  1752.18   Mean   :  1898.2   Mean   : 6.247  
##  3rd Qu.:   199.53   3rd Qu.:   222.71   3rd Qu.:   231.2   3rd Qu.: 7.648  
##  Max.   :213518.02   Max.   :279409.95   Max.   :724577.3   Max.   :17.704  
##   log2sample2      log2sample3     log2samplevsumasM1 log2samplevsumasM2
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.000     Min.   : 0.000    
##  1st Qu.: 4.025   1st Qu.: 4.007   1st Qu.: 4.154     1st Qu.: 4.270    
##  Median : 5.620   Median : 5.739   Median : 5.579     Median : 5.733    
##  Mean   : 6.187   Mean   : 6.152   Mean   : 6.222     Mean   : 6.256    
##  3rd Qu.: 7.805   3rd Qu.: 7.860   3rd Qu.: 7.637     3rd Qu.: 7.601    
##  Max.   :18.092   Max.   :19.467   Max.   :18.379     Max.   :17.155    
##  log2samplevsumasM3
##  Min.   : 0.000    
##  1st Qu.: 3.912    
##  Median : 5.798    
##  Mean   : 6.317    
##  3rd Qu.: 8.210    
##  Max.   :20.505

## [1] 6.24652
## [1] 2.884429
## [1]  0.4206592  0.7701346  1.3704911  0.7286438  0.1855978 -0.1532363

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -1.310539e-16 0.01425665
## sd    9.998983e-01 0.01008093
## Loglikelihood:  -6979.259   AIC:  13962.52   BIC:  13975.52 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Cálculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8877976 0.8234548
## 70 -0.9174931 0.9668385
## 75 -0.9827617 1.1346553
## 80 -1.0188953 1.3615138
## 85 -1.1000839 1.6923518
## 90 -1.1462228 2.1593960
## 95 -1.3906263 2.6069712
## 99 -2.1655999 3.1547856

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra 2

## [1] 6.18657
## [1] 3.14197
## [1]  8.759868  8.417110 10.916716  8.356324  6.087241

Log-normalizacion

## [1] 6.18657
## [1] 3.14197
## [1]  0.81900802  0.70991786  1.50547137  0.69057145 -0.03161338  0.06789964

Ajuste de modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -1.093861e-16 0.01425665
## sd    9.998983e-01 0.01008093
## Loglikelihood:  -6979.259   AIC:  13962.52   BIC:  13975.52 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

Calculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8936351 0.8467827
## 70 -0.8936351 0.9884290
## 75 -1.0112465 1.1344069
## 80 -1.0836965 1.3626690
## 85 -1.1697595 1.6777371
## 90 -1.2757625 2.1128235
## 95 -1.6121684 2.5115181
## 99 -1.9690098 3.0656774

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra 3

## [1] 6.152478
## [1] 3.158081
## [1]  8.904774  5.314000 11.052005  9.365349  5.680082

## [1] 6.152478
## [1] 3.158081
## [1]  0.87150918 -0.26550237  1.55142547  1.01734948 -0.14958317 -0.05720518

Ajustando Modelos

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 1.292160e-16 0.01425665
## sd   9.998983e-01 0.01008093
## Loglikelihood:  -6979.259   AIC:  13962.52   BIC:  13975.52 
## Correlation matrix:
##              mean           sd
## mean  1.00000e+00 -3.26782e-11
## sd   -3.26782e-11  1.00000e+00

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Calculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8811857 0.8359346
## 70 -0.9495345 0.9646852
## 75 -1.0299396 1.1073525
## 80 -1.1855441 1.3225658
## 85 -1.2519401 1.6081997
## 90 -1.4233556 2.0263238
## 95 -1.7009100 2.4893700
## 99 -1.9481699 3.1039165

Creación de histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsumasM1

## [1] 6.221901
## [1] 2.955459
## [1] 5.749419 6.889844 9.540979 9.462180 6.904367

## [1] 6.221901
## [1] 2.955459
## [1] -0.1598676  0.2260030  1.1230332  1.0963710  0.2309173 -0.1181501

Primer histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -9.853035e-17 0.01425665
## sd    9.998983e-01 0.01008093
## Loglikelihood:  -6979.259   AIC:  13962.52   BIC:  13975.52 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293

Calculo de cuantiles

##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8531906 0.8236011
## 70 -0.9004672 0.9332295
## 75 -0.9528182 1.1118993
## 80 -0.9528182 1.3280269
## 85 -1.0781339 1.6545578
## 90 -1.1553694 2.1045904
## 95 -1.3603164 2.6974724
## 99 -2.1052233 3.3025359

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsumasM2

## [1] 6.255602
## [1] 2.72471
## [1]  8.745886  8.538024 10.187333  7.740125  6.603402

Primer Histograma

## [1] 6.255602
## [1] 2.72471
## [1]  0.9139630  0.8376754  1.4429905  0.5448372  0.1276466 -0.1223761

Segundo Histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##          estimate Std. Error
## mean 1.626526e-16 0.01425665
## sd   9.998983e-01 0.01008093
## Loglikelihood:  -6979.259   AIC:  13962.52   BIC:  13975.52 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

Cálculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8779788 0.8200071
## 70 -0.9297388 0.9699103
## 75 -0.9871123 1.1247423
## 80 -1.0514667 1.3424300
## 85 -1.1247392 1.7188713
## 90 -1.2098054 2.1841922
## 95 -1.3112040 2.5940822
## 99 -1.8422575 3.1504680

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)

Muestra pEhExvsumasM3

## [1] 6.317092
## [1] 3.410882
## [1]  9.778619  5.482538 12.794720 10.625030  7.128629

** Primer histograma**

## [1] 6.317092
## [1] 3.410882
## [1]  1.0148481 -0.2446740  1.8991065  1.2629984  0.2379259 -0.3441057

Segundo histograma

Ajustando Modelo

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##           estimate Std. Error
## mean -2.967667e-17 0.01425665
## sd    9.998983e-01 0.01008093
## Loglikelihood:  -6979.259   AIC:  13962.52   BIC:  13975.52 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

Calculo de cuantiles

##       Cuantiles Ajuste
## 0.5%        -2.5758293
## 2.5%        -1.9599640
## 5%          -1.6448536
## 7.5%        -1.4395315
## 10%         -1.2815516
## 12.5%       -1.1503494
## 15%         -1.0364334
## 17.5%       -0.9345893
## 82.5%        0.9345893
## 85%          1.0364334
## 87.5%        1.1503494
## 90%          1.2815516
## 92.5%        1.4395315
## 95%          1.6448536
## 97.5%        1.9599640
## 99.5%        2.5758293
##        LimInf    LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293
##        LimInf    LimSup
## 65 -0.8628952 0.8884901
## 70 -0.9319289 1.0207444
## 75 -1.0144701 1.1904247
## 80 -1.1171230 1.3814634
## 85 -1.2529569 1.6102025
## 90 -1.4542681 1.9460444
## 95 -1.8520407 2.4110529
## 99 -1.8520407 3.0342081

Histogramas

Grafica Cuantiles del \(65\%\) y \(80\%\)

Grafica Cuantiles del \(70\%\) y \(85\%\)